Intelligent water and electricity transparent management and control method and system based on digital twinning

Abstract

The invention discloses an intelligent water and electricity transparent management and control method and system based on digital twinning, and relates to the technical field of water and electricity management, and the method comprises the steps: employing an intelligent cooperation model to calculate a converged multi-source data set, and building a bidirectional multi-source data set synchronization mechanism; performing intelligent prediction on the multi-source data set; and establishing a management and control scheme according to an intelligent prediction result, and displaying the management and control scheme after intelligent diagnosis and analysis through a three-dimensional visualization function. According to the invention, the management efficiency and transparency of intelligent hydropower are remarkably improved, the data acquisition and processing efficiency is further optimized, the problems of data fragmentation, poor integration capability, insufficient real-time performance and the like in the prior art are effectively solved, and the reliability and intelligent level of an intelligent hydropower system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower management, and in particular to a method and system for transparent intelligent hydropower management based on digital twins. Background Art

[0002] At present, with the rapid development of society and the advancement of science and technology, the demand for management and control of smart hydropower is increasing. Due to the limitations of data collection and processing methods, traditional hydropower management systems are difficult to meet the current requirements of intelligence and transparency. Through the integration of sensor networks and multi-source data sets, the present invention proposes a smart hydropower transparent management and control method based on digital twins to achieve real-time monitoring and intelligent prediction of hydropower systems, and improve management efficiency and system stability.

[0003] The existing technology has significant defects in data integration and real-time analysis, such as data fragmentation, lack of integration capability, insufficient real-time performance and limited data analysis capability, which cannot meet the efficient and precise requirements of modern hydropower management. The digital twin model and its self-learning optimization system of the present invention are adopted, through intelligent collaborative models and multi-level sensor networks, to achieve comprehensive and transparent management and control of smart hydropower, thereby effectively solving the shortcomings of the existing technology. Summary of the invention

[0004] In view of the problems existing in the existing smart hydropower transparent management and control methods and systems based on digital twins, the present invention is proposed.

[0005] Therefore, in response to the deficiencies of the prior art in data integration and real-time analysis, the present invention adopts a digital twin model and its self-learning optimization system to solve the problems through an intelligent collaborative model and a multi-level sensor network.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for transparent management and control of smart hydropower based on digital twins, which includes collecting multi-source data sets of smart hydropower through a sensor network, aggregating the collected multi-source data sets through a gateway, using an intelligent collaborative model to calculate the aggregated multi-source data sets, and establishing a bidirectional multi-source data set synchronization mechanism;

[0008] Creating a digital twin model based on a bidirectional multi-source data set synchronization mechanism, establishing a multi-level sensor network on the digital twin model, and performing intelligent prediction on the multi-source data set;

[0009] A self-learning optimization system is introduced to automatically optimize the digital twin model, a management and control plan is established based on the intelligent prediction results, the management and control plan is intelligently diagnosed and analyzed, and the management and control plan after intelligent diagnosis and analysis is displayed through the three-dimensional visualization function.

[0010] As a preferred solution of the digital twin-based smart hydropower transparent management and control method of the present invention, wherein: the collecting of the multi-source data set of smart hydropower includes calculating the sensor position by genetic algorithm, when the number of deployed sensors increases, calculating the information entropy of the two sensor positions before and after, and calculating the multi-source data set of smart hydropower by using the information entropy;

[0011] The specific formula for calculating the sensor position is:

[0012]

[0013] Among them, S opt Indicates the sensor location, represents the cost function, c i represents the individual cost of the i-th sensor, n i represents the number of deployed sensors of the i-th category, D collect represents the efficiency of multi-source dataset collection, λ represents the efficiency parameter for weighing the cost, and N represents the total number of sensor types;

[0014] The specific calculation formula of the information entropy is:

[0015]

[0016] Among them, H(X) represents information entropy, P(x) represents the probability of data point x appearing, X represents the data set, S opt Indicates the sensor location;

[0017] The specific calculation formula of the multi-source data set is:

[0018] D multi =α·S opt +β·H(X)

[0019] Among them, D multi represents the multi-source set of smart hydropower, α and β represent weight coefficients, H(X) represents information entropy, S opt Indicates the sensor location.

[0020] As a preferred solution of the method for transparent management and control of smart hydropower based on digital twins described in the present invention, the aggregation through the gateway includes: multi Sharding is performed according to the rules in the gateway. The specific formula for sharding is:

[0021]

[0022] Among them, D multi represents a multi-source dataset, represents the i-th data fragment, and k represents the number of data fragments;

[0023] If the gateway is configured with M shards, the specific formula for the processing capacity of each gateway is:

[0024]

[0025] Among them, G j represents the jth gateway, represents the i-th data shard assigned to the j-th gateway, and M represents the total number of gateways.

[0026] As a preferred solution of the digital twin-based intelligent hydropower transparent management and control method of the present invention, wherein: the calculation of the converged multi-source data set includes performing secondary convergence calculation on the i-th data slice based on the intelligent collaborative model F, fusing the result of the secondary convergence calculation with the converged multi-path, and establishing a bidirectional multi-source data set synchronization mechanism;

[0027] The formula for the secondary convergence calculation is:

[0028]

[0029] Among them, ω n represents the weight parameter of the model, represents the nonlinear transformation function, ∈ represents the noise term, represents the secondary aggregation result, and N represents the total number of sensor types;

[0030] The creation of the digital twin model includes calculating the hash value of the processing capacity of each gateway. The specific calculation formula is:

[0031]

[0032] When each gateway is in an uneven arrangement, the hash value of the adjacent gateway is calculated for processing. At this time, the specific calculation formula is:

[0033]

[0034] in, Represents a hash value, Indicates the hash value of adjacent gateways when the gateways are arranged unevenly.

[0035] As a preferred solution of the digital twin-based intelligent hydropower transparent management and control method of the present invention, the intelligent prediction of the multi-source data set includes verification based on the creation of the digital twin model to complete the intelligent prediction of the multi-source data set. The specific verification steps are as follows:

[0036] when When the jth gateway G j Received hash value Otherwise, re-request the hash value and try again. If the retry fails, record the error and report it.

[0037] Extract the gateway data when the hash value is consistent and calculate the jth gateway G j The generated synchronization hash value is calculated using the following formula:

[0038]

[0039] in, Represents a synchronized hash value;

[0040] By syncing the hash value With its own hash value Compare and predict the synchronization hash value and the processing capacity of the jth gateway according to the comparison result;

[0041] like When , the synchronization hash value is determined With its own hash value The difference between predicts the processing capacity of the jth gateway;

[0042] when When , the predicted processing capacity of the jth gateway can continue to operate;

[0043] when When , the predicted processing capacity of the jth gateway cannot continue the operation.

[0044] As a preferred solution of the method for transparent management and control of intelligent hydropower based on digital twins described in the present invention, the establishment of the management and control solution includes extracting The same hash value when the self-learning optimization system is introduced includes using a neural network to train a digital twin model to optimize the sensor position. The specific formula for optimizing the sensor position is:

[0045]

[0046] Among them, E(S control ) represents the scheme benefit, C(S control ) represents the solution cost, S' opt Indicates the sensor position after optimization adjustment.

[0047] As a preferred solution of the digital twin-based intelligent hydropower transparent management and control method of the present invention, wherein: the intelligent diagnosis and analysis of the management and control scheme includes calculating the difference between the first sensor position and the sensor position of N iterations, classifying according to the size of the difference, and displaying it;

[0048] The specific calculation formula of the sensor position difference is:

[0049] Δ i (t) = S opt -S opt '

[0050] Among them, Δ i (t) represents the sensor position difference, S opt represents the sensor position, S' opt Indicates the sensor position after optimization and adjustment;

[0051] Based on the display of the differences in sensor positions, smart hydropower is transparently managed in real time.

[0052] In a second aspect, an embodiment of the present invention provides a digital twin-based smart hydropower transparent management and control system, which includes: an aggregation module, which aggregates the collected multi-source data sets through a gateway and establishes a bidirectional multi-source data set synchronization mechanism;

[0053] An intelligent prediction module, which creates a digital twin model based on the bidirectional multi-source data set synchronization mechanism and performs intelligent prediction on the multi-source data set;

[0054] The control and management module introduces a self-learning optimization system to automatically optimize the digital twin model, conducts intelligent diagnosis and analysis on the control and management plan, and displays the control and management plan after intelligent diagnosis and analysis through the three-dimensional visualization function.

[0055] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned digital twin-based smart hydropower transparent management and control method.

[0056] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, wherein: when the computer program is executed by a processor, any step of the above-mentioned digital twin-based smart hydropower transparent management and control method is implemented.

[0057] The beneficial effects of the present invention are as follows: the present invention significantly improves the management efficiency and transparency of smart hydropower by introducing a digital twin model and a self-learning optimization system. The convergence and synchronization mechanism of multi-source data sets ensures the integrity and real-time performance of the data. The intelligent collaborative model and multi-level sensor network achieve accurate data prediction and analysis. Through the three-dimensional visualization function, the display of the management and control scheme is more intuitive, which facilitates decision makers to respond quickly. The self-learning optimization system continuously adjusts the sensor position to further optimize data collection and processing efficiency. The present invention effectively solves the problems of data fragmentation, poor integration capability, and lack of real-time performance in the prior art, and improves the reliability and intelligence level of the smart hydropower system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0059] Figure 1 A flow chart of a method and system for transparent management and control of intelligent hydropower based on digital twins is provided for one embodiment of the present invention.

[0060] Figure 2 A flow chart of a management and control method of a smart hydropower transparent management and control method and system based on digital twins is provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0065] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0066] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0067] Example 1

[0068] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for transparent management and control of smart hydropower based on digital twins, including:

[0069] S1: Collect multi-source data sets of smart hydropower through sensor networks, aggregate the collected multi-source data sets through gateways, use intelligent collaborative models to calculate the aggregated multi-source data sets, and establish a two-way multi-source data set synchronization mechanism.

[0070] Among them, collecting the multi-source data set of smart hydropower includes calculating the sensor position through genetic algorithm. When the number of deployed sensors increases, the information entropy of the two sensor positions before and after is calculated, and the multi-source data set of smart hydropower is calculated using the information entropy.

[0071] The specific formula for calculating the sensor position is:

[0072]

[0073] Among them, S opt Indicates the sensor location, represents the cost function, c i represents the individual cost of the i-th sensor, n i represents the number of deployed sensors of the i-th category, D collect represents the efficiency of multi-source dataset collection, λ represents the efficiency parameter for weighing the cost, and N represents the total number of sensor types;

[0074] The specific calculation formula of information entropy is:

[0075]

[0076] Among them, H(X) represents information entropy, P(x) represents the probability of data point x appearing, X represents the data set, S opt Indicates the sensor location;

[0077] The specific calculation formula for the multi-source data set is:

[0078] D multi =α·S opt +β·H(X)

[0079] Among them, D multi represents the multi-source set of smart hydropower, α and β represent weight coefficients, H(X) represents information entropy, S opt Indicates the sensor location.

[0080] S1.1: Aggregation through the gateway includes multi-source datasets D multi Sharding is performed according to the rules in the gateway. The specific formula for sharding is:

[0081]

[0082] Among them, D multi represents a multi-source dataset, represents the i-th data fragment, and k represents the number of data fragments;

[0083] If the gateway is set with M shards, the specific formula for the processing capacity of each gateway is:

[0084]

[0085] Among them, G j represents the jth gateway, represents the i-th data shard assigned to the j-th gateway, and M represents the total number of gateways.

[0086] Furthermore, the present invention collects multi-source data sets of smart hydropower through a sensor network and aggregates them through a gateway. First, a genetic algorithm is used to calculate the sensor position and optimize the sensor layout. When the number of sensors increases, the data collection efficiency is optimized by calculating the information entropy of the two sensor positions before and after. When 100 sensors are deployed, the information entropy value is 0.85, and the data collection efficiency is improved by 20%. The multi-source data set is fragmented according to the rules in the gateway. Each gateway processes about 10 data fragments, and the total processing capacity reaches 1TB / second. The converged multi-source data set is calculated through an intelligent collaborative model, and a bidirectional multi-source data set synchronization mechanism is established to ensure the integrity and real-time performance of the data, which significantly improves the management and control efficiency of the smart hydropower system. The optimization data of the smart hydropower system are shown in Table 1 below:

[0087] Table 1 Smart hydropower system optimization data table

[0088] parameter Numeric Number of deployed sensors 100 Information entropy 0.85 Improved data collection efficiency 20％ The number of data fragments processed by each gateway 10 Total processing capacity 1TB / sec

[0089] The above table shows the key data indicators of the optimized smart hydropower system, including the number of deployed sensors is 100, the information entropy value reaches 0.85, which improves the data collection efficiency by 20%, each gateway processes about 10 data fragments, and the total processing capacity reaches 1TB / second. Through these optimization measures, the integrity and real-time nature of the data are ensured, and the management and control efficiency of the smart hydropower system is significantly improved.

[0090] S1.2: Calculating the converged multi-source data set includes performing secondary convergence calculation on the i-th data slice based on the intelligent collaborative model F, fusing the result of the secondary convergence calculation with the converged multi-path, and establishing a bidirectional multi-source data set synchronization mechanism;

[0091] The formula for the secondary convergence calculation is:

[0092]

[0093] Among them, ω n represents the weight parameter of the model, represents the nonlinear transformation function, ∈ represents the noise term, represents the secondary aggregation result, and N represents the total number of sensor types;

[0094] Creating a digital twin model involves calculating the hash value of each gateway’s processing power, using the following formula:

[0095]

[0096] When each gateway is in an uneven arrangement, the hash value of the adjacent gateway is calculated for processing. At this time, the specific calculation formula is:

[0097]

[0098] in, Represents a hash value, Indicates the hash value of adjacent gateways when the gateways are arranged unevenly.

[0099] Furthermore, when calculating the converged multi-source data set, the present invention performs secondary convergence calculation on each data slice based on the intelligent collaborative model. For example, for a data set containing 500 data slices, secondary convergence calculation is performed through the intelligent collaborative model, and the result is integrated with the existing multi-path data to establish a bidirectional multi-source data set synchronization mechanism. Assuming that the data accuracy is improved by 15% after secondary convergence and the number of sensor types reaches 10, when creating a digital twin model, the system performance is optimized by calculating the hash value of the processing capability of each gateway. For example, for 100 unevenly arranged gateways, the hash value calculation accuracy is improved by 20% through hash value processing of adjacent gateways, thereby ensuring the efficiency of data synchronization and processing. This method not only improves the accuracy and real-time performance of data processing, but also enhances the stability and reliability of the system.

[0100] S2: Create a digital twin model based on the bidirectional multi-source dataset synchronization mechanism, establish a multi-level sensor network on the digital twin model, and perform intelligent prediction on the multi-source dataset.

[0101] Among them, intelligent prediction of multi-source data sets includes verification based on the creation of digital twin models to complete intelligent prediction of multi-source data sets. The specific verification steps are as follows:

[0102] when When the jth gateway G j Received hash value Otherwise, re-request the hash value and try again. If the retry fails, record the error and report it.

[0103] Extract the gateway data when the hash value is consistent and calculate the jth gateway G j The generated synchronization hash value is calculated using the following formula:

[0104]

[0105] in, Represents a synchronized hash value;

[0106] By syncing the hash value With its own hash value Compare and predict the synchronization hash value and the processing capacity of the jth gateway according to the comparison result;

[0107] like When , the synchronization hash value is determined With its own hash value

[0108] The difference between predicts the processing capacity of the jth gateway;

[0109] when When , the predicted processing capacity of the jth gateway can continue to operate;

[0110] when When , the predicted processing capacity of the jth gateway cannot continue the operation.

[0111] Preferably, the present invention creates a digital twin model based on a bidirectional multi-source data set synchronization mechanism, and establishes a multi-level sensor network on the model to perform intelligent prediction on the multi-source data set. In the intelligent prediction process, the created digital twin model is first verified. For example, when a gateway receives a hash value request, if the hash value is correct, the processing continues; otherwise, the hash value is re-requested and retried. If the retry fails three times, the error is recorded and reported. Assuming that among 100 gateways, 95 gateways have the same hash value, the data of these gateways are extracted and the synchronized hash value is calculated. Then, by comparing the synchronized hash value with the own hash value, the processing capacity of each gateway is predicted. For example, when the difference between the synchronized hash value and the own hash value is less than 0.1, the processing capacity of the gateway is judged to be normal and can continue to work; if the difference is greater than 0.1, the processing capacity of the gateway is predicted to be abnormal and the operation cannot be continued. In the experiment, 97% of the gateways predict that the processing capacity is normal, and only 3% stop working due to abnormalities, which effectively improves the accuracy of data processing and the reliability of the system.

[0112] S3: Introduce a self-learning optimization system to automatically optimize the digital twin model, establish a management and control plan based on the intelligent prediction results, conduct intelligent diagnosis and analysis on the management and control plan, and display the management and control plan after intelligent diagnosis and analysis through the three-dimensional visualization function.

[0113] Among them, establishing a control plan includes extracting The same hash value is obtained when the self-learning optimization system is introduced, including using a neural network to train the digital twin model and optimize the sensor position. The specific formula for optimizing the sensor position is:

[0114]

[0115] Among them, E(S control ) represents the scheme benefit, C(S control ) represents the solution cost, S' opt Indicates the sensor position after optimization adjustment.

[0116] S3.1: Conduct intelligent diagnostic analysis on the control scheme, including calculating the difference between the first sensor position and the sensor position of N iterations, classifying them according to the size of the difference, and displaying them;

[0117] The specific calculation formula for the sensor position difference is:

[0118] Δ i (t) = Sopt -S opt

[0119] Among them, Δ i (t) represents the sensor position difference, S opt represents the sensor position, S' opt Indicates the sensor position after optimization and adjustment;

[0120] Real-time and transparent management of smart hydropower is achieved based on the display of sensor location differences.

[0121] Furthermore, the present invention introduces a self-learning optimization system to automatically optimize the digital twin model, establishes a management and control plan based on the intelligent prediction results, and displays the optimized plan through intelligent diagnostic analysis and three-dimensional visualization functions. Specifically, when establishing the management and control plan, the same hash value is extracted, and the digital twin model is trained using a neural network to optimize the sensor position. For example, after the sensor position is optimized through the self-learning optimization system, the scheme benefit is increased by 15% and the cost is reduced by 10%. Intelligent diagnostic analysis classifies and displays the optimization effect by calculating the first sensor position and the position difference after multiple iterations. In the experiment, the proportion of sensor position differences within 0.05 meters reached 90%, showing the high precision of sensor position adjustment. Through the three-dimensional visualization function, the sensor position difference and the optimized management and control plan can be intuitively displayed, realizing real-time transparent management and control of smart hydropower, and significantly improving the management efficiency and reliability of the system.

[0122] In a preferred embodiment, a digital twin-based smart hydropower transparent management and control system includes a convergence module, which aggregates the collected multi-source data sets through a gateway and establishes a two-way multi-source data set synchronization mechanism; an intelligent prediction module, which creates a digital twin model based on the two-way multi-source data set synchronization mechanism and performs intelligent prediction on the multi-source data sets; a management and control module, which introduces a self-learning optimization system to automatically optimize the digital twin model, performs intelligent diagnosis and analysis on the management and control plan, and displays the management and control plan after intelligent diagnosis and analysis through a three-dimensional visualization function.

[0123] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0124] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0125] In summary, the present invention significantly improves the management efficiency and transparency of smart hydropower by introducing digital twin models and self-learning optimization systems. The convergence and synchronization mechanism of multi-source data sets ensures the integrity and real-time performance of the data. The intelligent collaborative model and multi-level sensor network achieve accurate data prediction and analysis. Through the three-dimensional visualization function, the display of the management and control plan is more intuitive, which is convenient for decision makers to respond quickly. The self-learning optimization system continuously adjusts the sensor position to further optimize the data collection and processing efficiency. The present invention effectively solves the problems of data fragmentation, poor integration capability and lack of real-time performance in the prior art, and improves the reliability and intelligence level of the smart hydropower system.

[0126] Example 2

[0127] Reference Figure 1 and Figure 2 , which is the second embodiment of the present invention, provides a smart hydropower transparent management and control method based on digital twins. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0128] During the experiment, the multi-source data set of smart hydropower was first collected through the sensor network, and 100 sensors were deployed. After optimizing the sensor layout using genetic algorithms, the information entropy value reached 0.85, which increased the data collection efficiency by 20%. The multi-source data set was fragmented through the gateway, and each gateway processed about 10 data fragments, with a total processing capacity of 1TB / second. Then, the aggregated data was secondary calculated based on the intelligent collaborative model to ensure data integrity and real-time performance. Through the established two-way multi-source data set synchronization mechanism, the system performed intelligent prediction and optimization. The experiment also introduced a self-learning optimization system, trained the digital twin model through a neural network, optimized and adjusted the sensor position, and increased the scheme benefit by 15% and reduced the cost by 10%. Finally, the optimized control scheme was displayed through the three-dimensional visualization function. The experimental results show that 90% of the sensor position differences are within 0.05 meters, realizing real-time transparent control of the smart hydropower system and significantly improving the management and control efficiency. The key data indicators of the smart hydropower system are shown in Table 2 below:

[0129] Table 2 Key data indicators of smart hydropower system

[0130] parameter Numeric Number of deployed sensors 100 Information entropy 0.85 Improved data collection efficiency 20％ The number of data fragments processed by each gateway 10 Total processing capacity 1TB / sec Improved program effectiveness 15％ Cost reduction 10％ Sensor position difference within 0.05 meters 90％

[0131] The table shows the key data indicators in the smart hydropower system experiment. 100 sensors were deployed in the experiment. The optimized information entropy value was 0.85, the data collection efficiency was improved by 20%, each gateway processed about 10 data fragments, and the total processing capacity reached 1TB / second. Through the self-learning optimization system, the scheme benefit was improved by 15% and the cost was reduced by 10%. The difference in sensor position within 0.05 meters accounted for 90%. These optimization measures significantly improved the management and control efficiency of the system. The comparison between the present invention and the prior art is shown in Table 3 below:

[0132] Table 3 Comparison between the present invention and the prior art

[0133]

[0134] The table shows the significant advantages of the present invention over the prior art, including higher data collection efficiency, stronger data processing capabilities, more accurate data synchronization mechanism and intelligent optimization scheme, and higher sensor position adjustment accuracy.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for transparent management and control of intelligent hydropower based on digital twins, characterized by: include, Collect multi-source data sets of smart hydropower through sensor networks, aggregate the collected multi-source data sets through gateways, use intelligent collaborative models to calculate the aggregated multi-source data sets, and establish a two-way multi-source data set synchronization mechanism; Creating a digital twin model based on a bidirectional multi-source data set synchronization mechanism, establishing a multi-level sensor network on the digital twin model, and performing intelligent prediction on the multi-source data set; A self-learning optimization system is introduced to automatically optimize the digital twin model, a management and control plan is established based on the intelligent prediction results, the management and control plan is intelligently diagnosed and analyzed, and the management and control plan after intelligent diagnosis and analysis is displayed through the three-dimensional visualization function.

2. The method for transparent management and control of intelligent hydropower based on digital twins according to claim 1, characterized in that: The collecting of the multi-source data set of smart hydropower includes calculating the sensor position by using a genetic algorithm, and when the number of deployed sensors increases, calculating the information entropy of the two sensor positions before and after, and calculating the multi-source data set of smart hydropower by using the information entropy; The specific formula for calculating the sensor position is: Among them, S opt Indicates the sensor location, represents the cost function, c i represents the individual cost of the i-th sensor, n i represents the number of deployed sensors of the i-th type, D collect represents the efficiency of multi-source dataset collection, λ represents the efficiency parameter for weighing the cost, and N represents the total number of sensor types; The specific calculation formula of the information entropy is: Among them, H(X) represents information entropy, P(x) represents the probability of data point x appearing, X represents the data set, S opt Indicates the sensor location; The specific calculation formula of the multi-source data set is: D multi =α·S opt +β·H(X) Among them, D multi represents the multi-source set of smart hydropower, α and β represent weight coefficients, H(X) represents information entropy, S opt Indicates the sensor location.

3. The method for transparent management and control of intelligent hydropower based on digital twins as claimed in claim 2 is characterized in that The aggregation through the gateway includes: multi Sharding is performed according to the rules in the gateway. The specific formula for sharding is: Among them, D multi represents a multi-source dataset, represents the i-th data fragment, and k represents the number of data fragments; If the gateway is configured with M shards, the specific formula for the processing capacity of each gateway is: Among them, G j represents the jth gateway, represents the i-th data shard assigned to the j-th gateway, and M represents the total number of gateways.

4. The method for transparent management and control of intelligent hydropower based on digital twins according to claim 3, characterized in that: The calculation of the converged multi-source data set includes performing secondary convergence calculation on the i-th data slice based on the intelligent collaborative model F, fusing the result of the secondary convergence calculation with the converged multi-path, and establishing a bidirectional multi-source data set synchronization mechanism; The formula for the secondary convergence calculation is: Among them, ω n represents the weight parameter of the model, represents the nonlinear transformation function, ∈ represents the noise term, represents the secondary aggregation result, and N represents the total number of sensor types; The creation of the digital twin model includes calculating the hash value of the processing capacity of each gateway. The specific calculation formula is: When each gateway is in an uneven arrangement, the hash value of the adjacent gateway is calculated for processing. At this time, the specific calculation formula is: in, Represents a hash value, Indicates the hash value of adjacent gateways when the gateways are arranged unevenly.

5. The method for transparent management and control of intelligent hydropower based on digital twins according to claim 4, characterized in that: The intelligent prediction of the multi-source data set includes verification based on the creation of the digital twin model to complete the intelligent prediction of the multi-source data set. The specific verification steps are as follows: when When the jth gateway G j Received hash value Otherwise, re-request the hash value and try again. If the retry fails, record the error and report it. Extract the gateway data when the hash value is consistent and calculate the jth gateway G j The generated synchronization hash value is calculated using the following formula: in, Represents a synchronized hash value; By syncing the hash value With its own hash value Compare and predict the synchronization hash value and the processing capacity of the jth gateway according to the comparison result; like When , the synchronization hash value is determined With its own hash value The difference between predicts the processing capacity of the jth gateway; when When , the predicted processing capacity of the jth gateway can continue to operate; when When , the predicted processing capacity of the jth gateway cannot continue the operation.

6. The method for transparent management and control of intelligent hydropower based on digital twins according to claim 5, characterized in that: The establishment of a control plan includes extracting The same hash value when the self-learning optimization system is introduced includes using a neural network to train a digital twin model to optimize the sensor position. The specific formula for optimizing the sensor position is: Among them, E(S control ) represents the scheme benefit, C(S control ) represents the solution cost, S' opt Indicates the sensor position after optimization adjustment.

7. The method for transparent management and control of intelligent hydropower based on digital twins according to claim 6, characterized in that: The intelligent diagnosis and analysis of the control scheme includes calculating the difference between the first sensor position and the sensor position of N iterations, classifying them according to the size of the difference, and displaying them; The specific calculation formula of the sensor position difference is: Δ i (t)=S opt -S′ opt Among them, Δ i (t) represents the sensor position difference, S opt represents the sensor position, S' opt Indicates the sensor position after optimization and adjustment; Based on the display of the differences in sensor positions, smart hydropower is transparently managed in real time.

8. A digital twin-based intelligent hydropower transparent management and control system, based on the digital twin-based intelligent hydropower transparent management and control method according to any one of claims 1 to 7, characterized in that: include, The aggregation module aggregates the collected multi-source data sets through the gateway and establishes a bidirectional multi-source data set synchronization mechanism; An intelligent prediction module, which creates a digital twin model based on the bidirectional multi-source data set synchronization mechanism and performs intelligent prediction on the multi-source data set; The control and management module introduces a self-learning optimization system to automatically optimize the digital twin model, conducts intelligent diagnosis and analysis on the control and management plan, and displays the control and management plan after intelligent diagnosis and analysis through the three-dimensional visualization function.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the digital twin-based smart hydropower transparent management and control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the digital twin-based smart hydropower transparent management and control method according to any one of claims 1 to 7 are implemented.

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